Inverse kinematics-based motion planning for dual-arm robot with orientation constraints

Liu, Shirong; Zhang, Botao; Yu, Changbin

doi:10.1177/1729881419836858

Cited by 14 publications

(4 citation statements)

References 47 publications

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“…The results show that our approach can provide significant benefits for motion planning with pose constraints. The direct projection method also has been successfully applied to the closed-chain motion planning with orientation constraints for a class of dual-arm robots in our work [45]. It is shown that the proposed method is a widely-fit method for constrained motion planning of the anthropomorphic manipulators.…”

Section: Discussionmentioning

confidence: 88%

Motion Planning With Pose Constraints Based on Direct Projection for Anthropomorphic Manipulators

Liu

Zhang

2020

IEEE Access

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This paper presents an efficient planning algorithm, called Direct-Projection Rapidly-exploring Random Tree (DP-RRT), to address the motion planning with end-effector pose constraints for anthropomorphic manipulators. The key of this planning problem is to find constraint-satisfying configurations on the constraint manifolds and connect them to generate a collision-free and smooth path. In the previous works, the configurations that satisfy pose constraints are generally calculated by the numerical iteration methods based on Jacobian projection techniques. However, such approaches have many technical challenges, such as joint limits and singularity, many numerical iterations and much computing time. In this work, we propose a Direct Projection method based on the analytic inverse kinematics (IK) that can directly project configurations onto the constraint manifolds instead of the numerical iteration methods. The proposed DP-RRT algorithm combines the Direct Projection method with the Rapidly-exploring Random Tree (RRT) algorithm, where the RRT algorithm is employed to explore the ambient space by growing tree branches, and the Direct Projection method is used to project the tree branches onto the constraint manifolds for constructing a constraint-satisfying path. As the analytic IK solver is used to calculate the constraint-satisfying configurations, the DP-RRT algorithm is characterized by high efficiency and no numerical iteration. Besides, avoiding joint limits and singularity, as well as the smoothness of the end-effector and the joints trajectory are also considered. The effectiveness of the proposed algorithm is demonstrated on the Willow Garage's PR2 simulation platform in a wide range of pose-constrained cases. INDEX TERMS Motion planning, direct projection, pose constraints, analytic inverse kinematics, anthropomorphic manipulator.

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Section: Discussionmentioning

confidence: 88%

Motion Planning With Pose Constraints Based on Direct Projection for Anthropomorphic Manipulators

Liu

Zhang

2020

IEEE Access

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“…To deal with closed kinematic chains, methods such as kinematics-based roadmap [12] and Random Loop Generator [13] decompose closed chains into open subsets, sampling from a subset and using IK to ensure that closure constraints are satisfied for the rest of the joints. Recently, IK-based motion planning for a dual-arm robot with orientation constraints was proposed in [14]. Based on some assumptions on the kinematic structure of the arms, an analytic IK solution was obtained for the wrist joints such that the orientation constraints were strictly satisfied.…”

Section: Related Workmentioning

confidence: 99%

Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

Acar,

Tee

2022

Preprint

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Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net (CGAN), to generate constraint-satisfying sample configurations. Instead of precomputed graphs, we use generative models conditioned on constraint parameters for approximating the constraint manifold. This approach allows for the efficient drawing of constraint-satisfying samples online without any need for modification of available sampling-based motion planning algorithms. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution. Simulations and experiments are also conducted for different constraint tasks on two robotic platforms.

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“…While in a non-confined space these methods are appropriate, in a highly confined space, the difficulty of selecting a goal configuration to which a valid, non-colliding path may be found increases. Another option is to provide an operator-selected goal configuration to the planner as used on: learning-based planners for redundant manipulators, [9], planners for redundant duel armed robots, [10], redundant manipulators with mobile bases, [11] and redundant manipulators in constrained work environments, [12]. Similarly, while this may be sufficient in some applications, for complex tasks in confined spaces the user cannot identify and define a valid manipulator goal configuration for every motion.…”

Section: Introductionmentioning

confidence: 99%

A MySQL Database for the Systematic Configuration Selection of Redundant Manipulators when Path Planning in Confined Spaces

Wood

Scott

Tzemanaki

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Redundant manipulators offer a continuum of joint configurations which satisfy a specific end-effector pose, an advantage when operating within confined spaces. This, however, challenges a controller to select a single goal configuration from a wide range when path planning. This paper outlines the use of the MySQL database management system to reduce randomisation in goal selection during redundant manipulator path planning in confined spaces. We outline a sampling method to envelope all configurations of a redundant manipulator and utilise it to generate a complete database of configurations. We demonstrate the application of this method to generate a large data-set of manipulator configurations for a KUKA LBR iiwa 14 equipped with a Robotiq 2F-85 gripper. With this database, the controller systematically selects goal configurations during 50 path planning scenarios within the confined space of a glovebox. We compare this to a randomised inverse kinematics search method to select goal configurations as a baseline. The database method achieves a 100% success rate in 42% of the scenarios attempted, in comparison to the baseline method which achieves a maximum success rate of just 25%. Our proposed method also produces repeatable paths, which are similar in length and link swept area for each attempt of the same scenario, whereas the baseline method generates a different path in every attempt.

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Inverse kinematics-based motion planning for dual-arm robot with orientation constraints

Cited by 14 publications

References 47 publications

Motion Planning With Pose Constraints Based on Direct Projection for Anthropomorphic Manipulators

Motion Planning With Pose Constraints Based on Direct Projection for Anthropomorphic Manipulators

Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

A MySQL Database for the Systematic Configuration Selection of Redundant Manipulators when Path Planning in Confined Spaces

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